Quantitative structural assay of a nerve graft

ABSTRACT

Techniques are described for determining the quality of a nerve graft by assessing quantitative structural characteristics of the nerve graft. Aspects of the techniques include obtaining an image identifying laminin-containing tissue in the nerve graft; creating a transformed image using a transformation function of an image processing application on the image; using an analysis function of the image processing application, analyzing the transformed image to identify one or more structures in accordance with one or more recognition criteria; and determining one or more structural characteristics of the nerve graft derived from a measurement of the one or more structures.

BACKGROUND

Peripheral nerves are often damaged or severed when a person suffers atraumatic injury. Direct nerve repair can be used for small gaps, butlarger gaps are sometimes repaired using nerve grafts. While the axonalsegment proximal to the site of the injury can regenerate new axonalsprouts, nonfunctional distal axon segments and their myelin sheaths arebelieved to have growth-inhibitory effects that curtail nerveregeneration. Substantial evidence indicates that the clearance ofnon-functional nerve elements improves axonal growth in the distal nervesegment.

One technique for improving the effectiveness of nerve grafts includesclearing the nerve graft of nonfunctional nerve elements beforesurgically installing the graft into the repair site. Nerve grafts, forexample, acellular grafts, having a structure and composition similar toa nerve fascicle, can assist in axonal regeneration by providing ascaffold through which new axon segments can grow. An acellular nervegraft, sometimes called a processed nerve graft, supports and directsthe growing axon segments with supporting structures, while providing apathway clear of axonal and myelin debris.

BRIEF SUMMARY

The subject invention provides materials and methods for determining thequality of a nerve graft by assessing quantitative structuralcharacteristics of the nerve graft. In certain embodiments, the methodsinvolve obtaining an image identifying laminin-containing tissue in thenerve graft; creating a transformed image using a transformationfunction of an image processing application on the image; using ananalysis function of the image processing application, analyzing thetransformed image to identify one or more structures in accordance withone or more recognition criteria; and determining one or more structuralcharacteristics of the nerve graft derived from a measurement of the oneor more structures.

In some embodiments, the structural characteristics are derived frommeasurements of the endoneurial tubes present in the fascicles of thenerve graft. In certain embodiments, structural characteristics include:the number of endoneurial tubes per area, the percent of endoneurialtube lumen per area, the total perimeter of endoneurial tube lumens perarea, or any combination thereof.

In some embodiments, the techniques may further comprise comparing thestructural characteristics to a qualitative assessment score; one ormore reference ranges indicating an acceptable structural characteristicof the nerve graft; a bioassay result of the nerve graft; or anycombination thereof.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Patent Office upon request andpayment of the necessary fee.

FIG. 1 shows an image of a slide having a cross-section of a peripheralnerve fiber with anti-laminin staining to highlight the endoneurialtubes and other nearby structures.

FIG. 2 shows an example procedural flow that may be used in someembodiments of the techniques.

FIG. 3A shows the effect of thresholding on an image of alaminin-stained endoneurial tube cross section.

FIG. 3B shows an example of the effect of particle analysis on athresholded image of a laminin-stained endoneurial tube cross section.

FIG. 4 shows an example embodiment comparing images of a nerve graft asvarious described techniques are performed.

FIGS. 5A-5C show scatter plots comparing various structuralcharacteristics to the historical qualitative histology score.

DETAILED DESCRIPTION

The degree to which a nerve graft is effective in promoting axon growthis believed to be related to the structural characteristics of the nervegraft; however, effective reproducible mechanisms of assessing thestructural characteristics of a nerve graft have been lacking. Thesubject invention provides techniques are described for determining thequality of a nerve graft by assessing quantitative structuralcharacteristics of the nerve graft.

In some embodiments, the structural characteristics are derived frommeasurements of the endoneurial tubes present in the fascicles of thenerve graft.

The outermost layer of the nerve cable is the epineurium, which is thelayer most often interacted with in peripheral nerve repair. In largernerve cables, the cable is subdivided into multiple fascicles, which aredefined by another connective tissue layer, the perineurium.“Endoneurial tubes” are the smallest, thinnest and innermost connectivetissue layer in peripheral nerve cables and may also be called theendoneurium, endoneurial channel, endoneurial sheath, or Henle's sheath.They are secreted by and around Schwann cells, which are ensheathingaxons. The course of the endoneurial tubes is generally longitudinalalong the course of the nerve cable except where fibers leave (or enter,in the case of communication branches between different nerve cables)the nerve cable. The endoneurial tube is a thin basement membraneprincipally consisting of a layer of Collagen IV with a layer of lamininon the interior surface.

FIG. 1 shows an image of a slide having a cross-section of a peripheralnerve fiber with anti-laminin immunostaining highlighting theendoneurial tubes.

Important aspects of the potency or bioactivity of a nerve graft are thegraft's structural integrity and structural characteristics. The greaterthe quantity and accessibility of the bioactive scaffold (laminin-coatedendoneurial tube geometry) present in the graft, the greater thebioactivity of the graft. The reason is that more bioactive scaffoldprovides more growth structures for axons and Schwann cells to extendonto.

Immunohistochemical staining (e.g., anti-laminin staining) can verifythe presence of laminin in the endoneurium. In embodiments of thetechniques of the subject invention, tissue from the processed nervegraft is stained using an anti-laminin antibody. The antibody may be,for example, a polyclonal antibody. Scanned images of the tissue undergoimage processing to determine the structural characteristics oflaminin-stained structures, such as endoneurial tubes, present in thetwo dimensional histology section.

Image processing in some embodiments can include selection ofsub-structures or regions of interest (e.g., fascicles) to furtherrefine those areas of the image where relevant structures are to befound. Selection can be manually performed by a human operator, forexample, by using a selection tool to outline the outer border of thestructure or region. Selection can also be automated by the imageprocessing application and in some cases verified by a human. In someembodiments, a “sampling window” can be used to define a subset of theimage. In some embodiments, the whole image may be utilized.

In some embodiments, image processing includes manipulating the image tomake structures of interest more visible for analysis. Types of imageprocessing used in some embodiments include thresholding the image inaccordance with various parameters.

In some embodiments, the identification of structures (e.g., endoneurialtubes) used to determine structural characteristics are in accordancewith one or more recognition criteria, such as the size and circularityof the structures.

In various embodiments, the structural characteristics can includemeasurements of (1) the number of endoneurial tubes in an area, (2) thepercent of endoneurial lumen in an area, and/or (3) the total perimeterof endoneurial tube lumens in an area. Better structural characteristicsresult in higher determined values. These methods provide quantitativeevidence of laminin presence and configuration in the endoneurium of thenerve grafts. Structural characteristics may be calculated over areascomprised of selected regions of interest and/or substructures, over asampling window, or over fixed areas.

In some embodiments, quantitative assessments of structural quality maybe correlated to qualitative assessments. The quantitative metrics canbe correlated to other metrics such as historically obtained qualitativescores from the same grafts. One method of qualitatively assessing thestructural integrity of a processed nerve allograft includesanti-laminin staining of the tissue and scoring the visual appearance ona qualitative ranking scale (e.g., a 1 to 5 scale divided into 0.5increments) in comparison to a positive control containing unprocessedperipheral nerve tissue. However, these methods are operator-dependentand are unable to precisely assess the quantity and availability ofbioactive scaffold using a reproducible methodology.

In some embodiments, the determined structural characteristics for agiven sample can be compared to a reference range for those structuralcharacteristics that indicate acceptable nerve graft quality. A nervegraft having values for structural characteristics that fall outside therange may be deemed to be of unacceptable quality.

In some embodiments, determined structural characteristics may becompared or correlated with results from a bioassay of the nerve graft.A bioassay may, for example, determine the bioactivity of a graft bymeasuring the extent of neurite growth in a cultured graft. In somecases, results from a bioassay may be correlated with the results fromthe structural characteristics to derive reference ranges for acceptablequality grafts.

FIG. 2 shows an example procedural flow that may be used in someembodiments of the techniques.

Some procedures may be performed using functions or features of an imageprocessing application, which is a computer program for manipulating thecharacteristics of digital images. An example of an image processingapplication that may be used in examples herein is Fiji (also known asImageJ). Furthermore, some procedures described in FIG. 2 may beoptional in some embodiments.

An image identifying laminin-containing tissue in a nerve graft isobtained (200). Generally, these nerve graft cross-sections (or,“sections”) are obtained by histological preparation of a sample of anerve graft, e.g., sectioning, fixing, staining, and mounting a sampleon a slide, which is then imaged using slide scanning hardware andsoftware. Such images can be a by-product or outcome of, for example, aproduction, processing, or quality control stage of readying the graftfor surgical implantation. In some cases, the images may have beenderived during one phase of production/processing, stored, and then maybe assessed using the described techniques at a different time.

In some embodiments, the nerve graft is a processed nerve allograft(human) intended for the surgical repair of peripheral nervediscontinuities to support regeneration across the defect. An example ofa processed nerve allograft is the Avance® Nerve Graft from AxoGen.Nerve allografts provide surgeons with a readily available nerve graftto repair peripheral nerves damaged by, for example, traumatic injury orremoved during a surgical procedure. A processed human nerve allograftis decellularized and processed, resulting in a surgical implant withthe natural structural pathways to guide axon regeneration. Such nervegrafts are available in a range of lengths and diameters, and worksimilarly to an autograft nerve without the comorbidities associatedwith secondary surgical site. Processing and decellularization of thenerve allograft clears much of the axonal and myelin debris so thatnerves may have an unimpeded pathway in which to regrow. Processing alsoremoves material and molecules that may potentially elicit a deleteriousimmune response in the recipient.

In some embodiments, the sections of nerve graft undergoimmunohistochemical staining to identify relevant structures in theimage. For example, anti-laminin staining of a section of a nerve graftcan result in high-contrast images showing the endoneurial tubes andother laminin-containing structures. In some cases, for example,staining can be performed with an immunoperoxidase stain using apolyclonal rabbit anti-laminin (Dako Z0097) with a polymer-basedsecondary system (Dako Envision and Rabbit HRP) and DAB(3,3′-diaminobenzidine) as the developing agent. However, other kinds ofstaining (such as a monoclonal antibody stain) or other structuraldemarcation techniques that identify an endoneurial tube or other keystructural components sufficiently in an image can be used.

Referring again to FIG. 1, anti-laminin staining of a nerve graft crosssection is depicted. In this Figure, laminin-containing structures areshown in brown. Laminin-containing structures that are important todetermining structural characteristics include the endoneurial tubes andperineurium (which defines the fascicle).

In some cases, the quality of staining is reviewed for its adequacy as afoundation for analysis of structural characteristics of the graft. Sucha review may be conducted by a human operator or quality controlpersonnel. Characteristics of quality anti-laminin staining include: thesection is largely free of artifacts and/or technical problems such aslifting; the staining color is brown (not blue, black, or other colors);the staining is localized to extracellular matrix structures expected tocontain laminin (endoneurial tubes and perineurial layers principally,but also the basal lamina surrounding fat droplets); and staining is notpresent or is minimal in the interior (lumens) of the endoneurial tubesand in the epineurium.

In some embodiments, techniques include selecting particularsub-structures, regions of interest, or sampling window(s) within theimage before further transformation and assessment of the structures(205). For instance, in some cases, particular substructures (e.g., thenerve fascicles) are selected to normalize the data to the area thatwould be expected to have the structural characteristics of interest.Selecting sub-structures or regions of interest in this way can alsoallow structural characteristics to be expressed in terms such as “perfascicle,” or as a ratio of fascicle area. In some cases, selection ofsubstructures can eliminate areas that may skew structuralcharacteristics or measurements therefrom (e.g., fat droplets areusually outside a fascicle).

Selection of regions of interest or substructures (e.g., fascicles) canbe performed manually or can be automated. In manual selection offascicles, for instance, a human operator might trace the outline offascicles using a region of interest selection tool in the imageprocessing application (for example, to select a region of interest inFiji/ImageJ, the “freehand selection tool” can be used to delineate anarea of interest which is then added to the region of interest listusing the manager tool). An automated selection of fascicles can use anautomated feature identification function, for example, to identifystructures having certain anti-laminin staining characteristics such asa brown color or a thickness indicating the perineurium. Automatedselection tasks may also be reviewed in a quality control step by ahuman operator and may be called “computer-assisted selection.”

In some cases, a sampling window can be used to select a subset of theimage. For example, a predetermined square area of the image (e.g., a100,000 pixel area in the center of the image) might be used. Use of afixed size sampling window can obviate the need for manual or automatedsubstructure selection steps, allowing the structural characteristics tobe determined in relation to a fixed area.

Whether an arbitrary selection of areas of interest in the image, asampling window, or the entire image is used, creating a transformedimage using a transformation function of the image processingapplication (210) can assist in the identification of relevantstructures. In some embodiments, transformation may include“thresholding,” in which an image is converted to binary and imagepixels meeting threshold conditions are selected.

FIG. 3A shows the effect of thresholding on an image of alaminin-stained endoneurial tube cross section. In FIG. 3A, alaminin-stained area 300 of an image is shown. Thresholding the image300 produces a binary (e.g., black and white) image 310.

Thresholding the image 300 in FIG. 3A may be performed in imageprocessing applications such as Fiji/ImageJ. Various settings may beapplied to perform the thresholding, such as a threshold method,threshold color, color space, and background. The threshold operationwhich results in image 310 uses the “default” threshold method, “black &white” threshold color, “HSB” color space, and modifies the backgroundcolor from white to black.

Thresholding may not need to be adjusted from the default settings inmany cases. Sometimes, however, additional adjustments (e.g., a manualadjustment of a “brightness” control by the human operator) may beperformed to obtain quality thresholding. Some characteristics ofquality thresholding include: primarily the areas staining dark brown(e.g., the endoneurial tubes) are thresholded; and areas with lightstaining or with Hematoxylin counterstaining have only occasional pixelsthresholded.

In some embodiments, the image may be converted to a differentrepresentation such as an 8-bit image. In some cases, transformation ofthe image may include converting the image to a different file format,such as the TIFF format. Naturally, such transformations are dependenton the image processing application chosen in a given embodiment and areintended to be exemplary rather than limiting.

Using the image processing application, the transformed image isanalyzed to identify one or more structure in accordance with one ormore recognition criteria (220). Structures (and measurements ofstructures) that may be of interest in determining structuralcharacteristics include, for example, the endoneurial tubes, the lumensof endoneurial tubes (i.e., the enclosed area of space inside spaceformed by the outer tubular structure of the endoneurium), the perimeterof the endoneurial tube or its lumen, and the area of the endoneurialtube lumen.

In some embodiments, the analysis of the transformed image can includethe use of, for example, a “particle analysis” feature of an imageprocessing application (particle analysis is the term used inFiji/ImageJ, but it should be appreciated by practitioners in the artthat different image processing applications can have equivalentfeatures and functions with different names). A particle analysisfeature can be used to identify structures having certaincharacteristics and to derive measurements from those identifiedstructures.

A recognition criterion is a requirement that a condition or property ofthe structure be satisfied in order for the structure to be recognizedas an entity of interest for identification. For example, when using a“particle analysis” function to identify structures, the recognitioncriterion might require the structure to have certain characteristics tobe recognized as a particle. These recognition criteria can beintroduced by using features of the image processing application to setconstraints on the identification function or to eliminatenon-conforming structures from the analysis.

Endoneurial tubes are roughly circular by nature (i.e., they conform tothe ensheathing Schwann cells), but due to the biological nature of thesource material and the fact that the observations are being made afterhistological preparation and sectioning, the endoneurial tubes may notbe completely circular as observed on the slide. Instead, the tubes mayappear flattened or elongated in cross-section.

In some embodiments, a recognition criterion can include a requirementfor a “circularity” of the structure. Circularity is a measure of thesimilarity of the geometry of a structure to that of a circle(mathematically, circularity can be defined as 4*π*(area/perimeter^2)).In principle, the circularity of a structure ranges from 0 to 1. Inpreferred embodiments, a recognition condition for circularity rangesfrom 0.5 to 1.0.

A recognition criterion for “size” can be used to filter out structuresthat are not of interest because they are larger or smaller than thestructures being identified. In embodiments where endoneurial tubes arethe structures being identified, setting a size criterion can eliminatenon-endoneurial structures that also have laminin. For example, thebasal laminae of fat droplets and the perineurium of the fasciclesthemselves may in some cases be filtered out of the analysis due totheir size. In preferred embodiments, the size criterion for thestructures may range from about 4.8 microns to about 16 microns indiameter. Example 1, below, outlines a procedure by which differentrecognition criteria may be tested for their usefulness in identifyingstructures.

FIG. 3B shows an example, in Fiji, of the effect of particle analysis ona thresholded image of a laminin-stained endoneurial tube cross section.In FIG. 3B, a thresholded image 350 is shown. Particle analysis on theimage 350 produces an image 360 where relevant structures have beenidentified (in the image, the relevant structures are colored in cyan,as the background is black).

Returning to FIG. 2, one or more structural characteristics of the nervegraft derived from a measurement of the one or more structures isdetermined (230). Once structures of interest have been identified,measurements can be performed on the identified structures (e.g., theirarea, perimeter, number, etc., as noted above) and calculations can bemade from the measurements to determine the structural characteristicsof the nerve graft.

Generally, the structural characteristics of relevance are those thatindicate the amount and accessibility of bioactive scaffold in thegraft. These structural characteristics may be derived from measurementand computation of the structures that were identified from thetransformed image. For instance, the structural characteristics caninclude measurements of (1) the number of endoneurial tubes per area,(2) the percent of endoneurial lumen per area, and/or (3) the totalperimeter of endoneurial tube lumens per area.

Some structural characteristics may be determined in reference to anarea. An area may contain a fixed number of absolute or relative units.Such an area may be measured, for example, in relative units (such as anarea of pixels, or pixel² for clarity in cases where a length in pixelsis also used, which might have a varying true size depending oncharacteristics of the image scanner, image format, or displaytechnology) or in absolute units, like microns². For instance, asampling window of a fixed number of units (e.g., 10,000 pixel²) mightbe taken from an image and the structural characteristics determined inreference to the sampling window. In another aspect, the area can denoteone or more regions of interest within a larger area, like a preselectedset of fascicles having certain sizes or visual characteristics. Iffascicles were preselected (either manually or computer-assisted) instep 205, the area used to compute structural characteristics might be,for example, per each fascicle or per total area of fascicles in asample.

One example of a structural characteristic, the number of endoneurialtubes per area, can be calculated by counting the number of tubes anddividing by the area. As noted, this characteristic can be calculatedwith the area being, e.g., an area of a fixed number of units ofabsolute or relative size, per-fascicle, and/or a total fascicle area.

Another example of a structural characteristic, the percent ofendoneurial lumen per area, can be calculated by obtaining the area ofeach of the identified structures (i.e., endoneurial tube lumens),summing the lumen areas, and dividing by the area. As noted, thischaracteristic can be calculated with the area being, e.g., an area of afixed number of units of absolute or relative size, the area of afascicle, and/or a total fascicle area for a sample.

Another example of a structural characteristic, the total perimeter ofendoneurial tube lumens per area, can be calculated by obtaining theperimeter of each of the identified structures (i.e., endoneurial tubelumens), summing the perimeters, and dividing by the area. As theidentified structures (e.g., the particles) are the lumens of theendoneurial tubes, a measurement of their perimeter corresponds tomeasurement of the perimeter of the laminin-containing inner surface ofthe endoneurial tube. As noted, this characteristic can be calculatedwith the area being, for example, an area of a fixed number of units ofabsolute or relative size, the area of a fascicle, and/or a totalfascicle area for a sample.

In some embodiments, the structural characteristics are weighted byfascicle size. Weighting, in reference to handling the determination ofa test statistic from multiple fascicles of different sizes, refers toincreasing the importance of larger fascicles for the determination ofthe test statistic for the entire section to account for their largersize (i.e. the test statistic of the section is the average of the teststatistic multiplied by the relative fascicle area for weighted vs. theaverage of the test statistic only for unweighted). Weighting a perfascicle result average in this manner may be equivalent to converting aper fascicle result average into a per total fascicle area average.

FIG. 4 shows an example embodiment comparing images of a nerve graft asvarious described techniques are performed. The nerve graft in thisexample is an Avance® nerve graft from AxoGen, Inc. In FIG. 4, onecolumn of images shows “acceptable structure” and a second column shows“unacceptable structure.” The column labeled acceptable structure showsthe original staining, thresholded, and analyzed images from a nervegraft that originally passed a qualitative assessment by a humanoperator. The column labeled unacceptable structure shows the originalstaining, thresholded, and analyzed images for a nerve graft that didnot pass a qualitative assessment. A view of each original stained imageand the images resulting from the transformation and particle analysissteps are shown. After analysis, a determination of structuralcharacteristics showed that the acceptable graft had endoneurial tubelumens comprising 30.4% of the fascicle area and that the unacceptablegraft had endoneurial tube lumens comprising only 6.7% of the fasciclearea.

EXPERIMENTS AND EXAMPLES

Following are examples illustrating procedures for practicing thetechniques disclosed herein. Advantages of the techniques may beillustrated from results obtained from one or more of these examples.Examples may also depict experimental conditions to refine thecharacteristics of certain method parameters. These examples andexperiments should not be construed as limiting.

Example 1

An embodiment of the invention was constructed to experimentally derivecertain ranges and parameters. As noted in the described method flow,images of samples containing a cross-section of a nerve graft wereobtained. Experimental conditions included alternative options forseveral parameters, which were then compared for closeness of fit to aqualitative histology score determined from the same sample images.

The laminin histology images of eleven (11) nerve graft lots comprisingAvance® nerve grafts from AxoGen, Inc. were assessed. The lots included33 large diameter (3-5 mm) and 33 small diameter (1-3 mm) samples.Images were derived from slides scanned into ImageScope from Aperio. Inthis case, the images were examined by an operator for the quality ofthe anti-laminin staining.

In this example embodiment, the fascicles were selected using an imageprocessing application. Here, the image processing application is Fiji(also known as ImageJ). The fascicles were selected using two methodsthat are evaluated as parameters: manual selection using a freehandselection tool in Fiji, and computer-assisted selection using a Fijimacro followed by a quality review and correction by a human operator.Results of the two techniques are compared below.

In this example, transformation of the image using the image processingapplication (here, Fiji) includes applying thresholding settings to theimage. Thresholding may enhance or reduce certain characteristics of theimage so that the image processing application can better analyze thestructures (e.g., the endoneurial tubes) depicted in the image. Initialthresholding settings include using the image processing application's“default” method; setting the threshold color to “black and white”;setting the color space to “HSB”; and setting the background to dark.The brightness of the transformed image may also be adjusted. Here,transformation of the image also includes converting the image to an8-bit representation.

Structures (e.g., the endoneurial tubes in the fascicles) wereidentified in this example using the “particle analysis” capability ofthe image processing application (here, Fiji). The particle analysisfeature identifies structures by virtue of its ability to recognizediscrete objects in the image because those objects were highlighted byimmunohistochemical staining and, in some cases, because imagetransformation settings make the staining more discernible to the imageprocessing application. Furthermore, when substructures, regions ofinterest, or sampling windows are selected, the analysis may be carriedout only within those regions.

In the example, settings for particle analysis function includedrecognition criteria for size and circularity and settings to “includeholes” and “exclude on edges.” Measurement settings include “area,”“perimeter,” and “integrated density.”

Example 1 utilizes two recognition criteria for determining a structure:size and circularity. A total of 32 different combinations of size andcircularity are shown in Table 1 below. The size indicates an area, inpixels, of the structure. In this case, an image pixel equals 0.495microns in accordance with the Aperio slide scanner settings.

TABLE 1 Criteria for Endoneurial tube recognition Criteria set Size(pixels{circumflex over ( )}2) Circularity 1 20-820 0.3-1.0 2 75-8200.3-1.0 3 20-1050 0.3-1.0 4 75-1050 0.3-1.0 5 20-1300 0.3-1.0 6 75-13000.3-1.0 7 20-2000 0.3-1.0 8 75-2000 0.3-1.0 9 20-820 0.4-1.0 10 75-8200.4-1.0 11 20-1050 0.4-1.0 12 75-1050 0.4-1.0 13 20-1300 0.4-1.0 1475-1300 0.4-1.0 15 20-2000 0.4-1.0 16 75-2000 0.4-1.0 17 20-820 0.5-1.018 75-820 0.5-1.0 19 20-1050 0.5-1.0 20 75-1050 0.5-1.0 21 20-13000.5-1.0 22 75-1300 0.5-1.0 23 20-2000 0.5-1.0 24 75-2000 0.5-1.0 2520-820 0.6-1.0 26 75-820 0.6-1.0 27 20-1050 0.6-1.0 28 75-1050 0.6-1.029 20-1300 0.6-1.0 30 75-1300 0.6-1.0 31 20-2000 0.6-1.0 32 75-20000.6-1.0

In Example 1, three structural characteristics were determined from therecognized endoneurial tubes: the number of endoneurial tubes in a100,000 pixel area, the percent of endoneurial tube lumen in an area,and the total perimeter of endoneurial tube lumens in a 100,000 pixelarea.

In this example, weighting was applied as an experimental parameter. Asnoted, weighting may convert a per fascicle test statistic into a pertotal fascicle area test statistic.

As an objective of Example 1 was to assess varying parameters of thetechniques, effects of the varied parameters are discussed. Outcomes ofalternative parameter choices were evaluated by comparing the values oftheir “goodness of fit” with historical qualitative histology scores(e.g., R² values). In this example, the qualitative histology score is arating by a human evaluator on a 1 to 5 scale divided into 0.5increments that compares the appearance of laminin in a test sample ofnerve graft tissue against a positive control containing unprocessedperipheral nerve tissue. A higher score indicates a closer fit, i.e.,the appearance of more bioactive scaffold.

“R²” (or R^2) is the Coefficient of Determination, a measure of the“goodness of fit” of an experimental vs. theoretical/modeled data set.Mathematically, R²=1−[sum((yi-fi)^2)/sum((yi-avgy)^2)] where “y” is theexperimental data, “f” is the modeled data, “i” is the counter for thedataset (i.e. “i” goes from 1 to the number of datapoints), and “avgy”is the average of “y” over the full data set.

Little effect of weighting for fascicle/selection area was noted.However, weighting the results, as described, by totalfascicle/selection area did result in slightly better correlation to thehistorical histological scoring.

Assisted area selection was equivalent to the completely manual methodas evidenced by the similarity of R² values. The assisted area selectionmethod has the analyst review every selection and correct it ifnecessary. Though the assisted method tended not to include some of thesmallest fascicles, it is mostly equivalent to the completely manualmethod. This would be expected from the great similarity of the areasselected by both methods (R²=0.995 comparing total area per section).

The data collected on the number of endoneurial tubes indicates thatusing a lower particle limit of 20 pixels leads to selecting featuresthat are not associated with historical histological scoring (i.e.lowers the correlation). Thus, a preferred lower limit for the “size”recognition criterion is 75 pixels (˜5 micron diameter).

The data collected on % area (and perimeter) show that use of an upperparticle limit of 1,300 and above leads to selecting features that arenot associated with historical histological scoring (i.e. lowers thecorrelation). Thus, a preferred upper limit for the “size” recognitioncriterion is below 1,300 pixels (e.g., 820 or 1050 pixels; ˜16 or ˜18microns in diameter).

The circularities were roughly similar for # of tubes and % area, butthe 0.3-1.0 and 0.4-1.0 circularity ranges were less stable. Thus, apreferred circularity range is 0.5-1.0.

All three structural characteristics in this example (# of tubes, %area, and perimeter of tubes) gave broadly similar results, with somedifferences depending on the particle analysis method.

Example 2

An embodiment of the invention was developed to experimentally assessthe closeness of fit of certain described techniques to the qualitativehistorical histology score determined from the same sample images. Tosummarize, Example 2 used specific ranges for the size and circularityrecognition conditions, and assisted selection, and compared threestructural characteristics against a historical qualitative score forgoodness of fit.

As noted in the described method flow, an image of a sample containing across-section of a nerve graft was obtained. In Example 2, thirty-twolots of Avance® Nerve Graft from AxoGen, Inc. were assessed; the lotsincluded four lots that did not pass the qualitative historicalhistology structural acceptance criteria. Result analysis examined thecorrelation between historical scoring data and three quantifiablestructural characteristics. The data was assessed by comparing thehistorical score for a given sample (e.g., from a single graft, alsoknown as a “section”) to each of the three structural characteristics.In addition, the data was assessed by comparing the historical scoreaverage for a lot (average of scores for 6 samples with each sample froma separate graft) versus the three structural characteristics for thesame lot. In summary, the results found that, for individual samples,comparison to the perimeter of endoneurial tubes provided the best fit(R²=0.622) and for the lot average, the percent endoneurial lumen areaprovided the best fit (R²=0.581).

In this embodiment of the described techniques, the following parametersand conditions were used: The areas of all fascicles in a section wereoutlined in Fiji using an initial computer selection followed by amanual inspection and correction, when necessary (i.e.,computer-assisted).

Transformation of the image using Fiji included applying thresholdingsettings to the image. Initial thresholding settings include using theimage processing application's “default” method; setting the thresholdcolor to “black and white”; setting the color space to “HSB”; andsetting the background to dark. The brightness of the transformed imagemay also be adjusted. Transformation of the image also includesconverting the image to an 8-bit representation.

Endoneurial tubes in the fascicles were identified using the “particleanalysis” capability of Fiji. Recognition criteria for performing theparticle analysis included size ranges and circularity ranges. The sizecriterion was set to identify structures from 75 to 820 pixel in area.The circularity criterion was set to identify structures having a0.5-1.0 circularity range. In this example, settings for particleanalysis function included settings to “include holes” and “exclude onedges.” Measurement settings include “area,” “perimeter,” and“integrated density.”

Three structural characteristics were determined from the recognizedendoneurial tubes: the number of endoneurial tubes in a 100,000 pixelarea, the percent of endoneurial tube lumen in an area, and the totalperimeter of endoneurial tube lumens in a 100,000 pixel area.

Note that a 100,000 pixel area is equal to 24,502.5 square microns (or˜0.025 square millimeters). The units of the test statistic are linearpixels (i.e. length of pixels) with one pixel being 0.495 microns inlength for Aperio ImageScope.

Weighting based on the size of the fascicle was applied in thecalculation of structural characteristics.

As noted, experimental data was assessed by comparing the historicalscore for a given sample or set of samples to each of the threestructural characteristics for the sample/set. The mathematical“goodness of fit” (or R²) between the experimental and modeled data setwas calculated as part of the assessment. Results are described belowand in the FIGS. 5A-5C.

FIG. 5A shows scatter plots comparing the number of endoneurial tubesstructural characteristic to the historical histology score for allsections (individual samples) and all lots examined, respectively. TheR² value for the section data set is 0.551, and the R² value for the lotdata set is 0.5118.

FIG. 5B shows scatter plots comparing the percent endoneurial tube lumenstructural characteristic to the historical histology score for allsections (individual samples) and all lots examined, respectively. TheR² value for the section data set is 0.6121, and the R² value for thelot data set is 0.5814.

FIG. 5C shows scatter plots comparing the perimeter of endoneurial tubesstructural characteristic to the historical histology score for allsections (individual samples) and all lots examined, respectively. TheR² value for the section data set is 0.622, and the R² value for the lotdata set is 0.5722.

Table 2 shows the Pearson Correlation Coefficients for historicalhistology scores in comparison to the structural characteristics for thesamples. Note: these are correlation coefficients (“R”) not coefficientsof determination (“R²”) as shown in the plots.

TABLE 2 Historical Number of Percent Perimeter Histology tubes tubelumen of tubes Historical Histology 1 NA NA NA Number of tubes 0.742 1NA NA Percent tube lumen 0.782 0.899 1 NA Perimeter 0.789 0.972 0.975 1

To summarize, for experimental results derived from this embodiment, theperimeter of endoneurial tubes structural characteristic is a slightlybetter match to a historical qualitative analysis of graft structuralquality. Two reasons are posited for this result. First, the perimeterstructural characteristic does not change if the circular structurecollapses during histological processing. Second, the perimeter of theoutside of the lumen is a direct measurement of the interior surface ofthe endoneurial tube, which is coated with laminin, and presumably thequantity of accessible laminin is a key bioactive substance forfostering neurite regeneration in a graft.

It should be understood that the examples and embodiments describedherein are for illustrative purposes only and that various modificationsor changes in light thereof will be suggested to persons skilled in theart and are to be included within the spirit and purview of thisapplication.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims and other equivalent features and acts are intended to be withinthe scope of the claims.

What is claimed is:
 1. A method for assessing the quality of a nervegraft, the method comprising: obtaining an image identifyinglaminin-containing tissue in the nerve graft; creating a transformedimage using a transformation function of an image processing applicationon the image; using an analysis function of the image processingapplication, analyzing the transformed image to identify one or morestructures in accordance with one or more recognition criteria;determining one or more structural characteristics of the nerve graftderived from a measurement of the one or more structures; and assessingthe quality of the nerve graft based on the determined one or morestructural characteristics, wherein the one or more recognition criteriacomprises a size range of the one or more structures and wherein thesize range is within a range from about 4.84 microns in diameter toabout 16 microns in diameter.
 2. The method of claim 1, wherein, beforecreating the transformed image, the method further comprises selectingone or more of an area of interest and a sampling window to delineate aselected image area, wherein the analyzing of the transformed image isperformed only on the selected image area.
 3. The method of claim 2,wherein the area of interest comprises a nerve fascicle.
 4. The methodof claim 1, wherein the one or more structures comprise an endoneurialtube.
 5. The method of claim 1, wherein creating the transformed imagecomprises applying thresholding to the image.
 6. The method of claim 5,wherein applying the thresholding comprises applying one or more of athreshold method, a threshold color, a color space, and a darkbackground.
 7. The method of claim 1, wherein the one or morerecognition criteria comprise a circularity range of the one or morestructures.
 8. The method of claim 7, wherein the circularity range isfrom about 0.5 to about 1.0.
 9. The method of claim 1, wherein the oneor more structural characteristics comprise the number of endoneurialtubes per area.
 10. The method of claim 1, wherein the one or morestructural characteristics comprise the percent of endoneurial tubelumen per area.
 11. The method of claim 1, wherein the one or morestructural characteristics comprise the total perimeter of endoneurialtube lumens per area.
 12. The method of claim 1, further comprising:comparing the one or more structural characteristics to a qualitativeassessment score.
 13. The method of claim 1, further comprising:comparing the one or more structural characteristics to one or morereference ranges indicating an acceptable structural characteristic ofthe nerve graft.
 14. The method of claim 1, further comprising:comparing the one or more structural characteristics to a bioassayresult of the nerve graft.
 15. A method for assessing the structuralquality of a nerve graft, the method comprising: obtaining an image oftissue, the image depicting a cross-section of the nerve graft, whereinthe cross-section is treated with a stain that indicates the presence oflaminin; selecting, using an image processing application, one or morenerve fascicles on the image; using the image processing application,creating a thresholded image, wherein the thresholded imagedistinguishes one or more visual aspects of the image; using a particleanalysis feature of the image processing application on the thresholdedimage, identifying one or more endoneurial tubes contained within theboundary of the one or more nerve fascicles, wherein the particleanalysis feature identifies the one or more endoneurial tubes inaccordance with one or more recognition criteria; determining one ormore structural characteristics of the nerve graft derived from ameasurement of the one or more endoneurial tubes; and assessing thestructural quality of the nerve graft based on the determined one ormore structural characteristics.
 16. The method of claim 15, wherein theone or more recognition criteria comprise a size range of the one ormore endoneurial tubes, wherein the size range is from about 4.84microns in diameter to about 16 microns in diameter.
 17. The method ofclaim 15, wherein the one or more recognition criteria comprise acircularity range of the one or more endoneurial tubes, wherein thecircularity range is from about 0.5 to about 1.0.
 18. The method ofclaim 15, wherein the one or more structural characteristics is one ormore of: the number of endoneurial tubes per area, the percent ofendoneurial tube lumens per area, and the total perimeter of endoneurialtube lumens per area.
 19. The method of claim 15, further comprising:comparing the one or more structural characteristics to one or more of:a qualitative assessment score; one or more reference ranges indicatingan acceptable structural characteristic of the nerve graft; and abioassay result of the nerve graft.
 20. The method of claim 15, whereinthe stain is an immunoperoxidase stain.
 21. A method for assessing thequality of a nerve graft, the method comprising: obtaining an imageidentifying laminin-containing tissue in the nerve graft; creating atransformed image using a transformation function of an image processingapplication on the image; using an analysis function of the imageprocessing application, analyzing the transformed image to identify oneor more structures in accordance with one or more recognition criteria;determining one or more structural characteristics of the nerve graftderived from a measurement of the one or more structures; and assessingthe quality of the nerve graft based on the determined one or morestructural characteristics, and wherein the one or more recognitioncriteria comprise a circularity range of the one or more structures, andwherein the circularity range is within the range from about 0.5 toabout 1.0.
 22. A method for assessing the quality of a nerve graft, themethod comprising: obtaining an image identifying laminin-containingtissue in the nerve graft; creating a transformed image using atransformation function of an image processing application on the image;using an analysis function of the image processing application,analyzing the transformed image to identify one or more structures inaccordance with one or more recognition criteria; determining one ormore structural characteristics of the nerve graft derived from ameasurement of the one or more structures; and assessing the quality ofthe nerve graft based on the determined one or more structuralcharacteristics, and wherein the one or more structural characteristicscomprise the total perimeter of endoneurial tube lumens per area.